rganeeva-ds2-solution.ipynb

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     "text": [
      "/kaggle/input/gb-credit-default/sample_submission.csv\n",
      "/kaggle/input/gb-credit-default/train.csv\n",
      "/kaggle/input/gb-credit-default/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, f1_score, precision_score, recall_score\n",
    "import catboost as catb\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f15c4ff7",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "sns.set(style='whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.5)  \n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "pd.set_option('display.max_rows', 50)\n",
    "plt.rcParams['figure.figsize'] = (6, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e796246",
   "metadata": {
    "papermill": {
     "duration": 0.017138,
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     "start_time": "2021-08-04T12:43:44.737247",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Загрузка датасетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39c26c17",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7500, 17), (2500, 16))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRAIN_DATASET_PATH = '../input/gb-credit-default/train.csv'\n",
    "TEST_DATASET_PATH = '../input/gb-credit-default/test.csv'\n",
    "SUBMIT_DATASET_PATH = '../input/gb-credit-default/sample_submission.csv'\n",
    "\n",
    "df_train = pd.read_csv(TRAIN_DATASET_PATH)\n",
    "df_test = pd.read_csv(TEST_DATASET_PATH)\n",
    "submit = pd.read_csv(SUBMIT_DATASET_PATH)\n",
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6cc198",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "# Обозначение переменных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60924f19",
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    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Категориальные признаки: ['Home Ownership', 'Years in current job', 'Tax Liens', 'Number of Credit Problems', 'Bankruptcies', 'Purpose', 'Term']\n",
      "Количественные признаки: ['Annual Income', 'Number of Open Accounts', 'Years of Credit History', 'Maximum Open Credit', 'Months since last delinquent', 'Current Loan Amount', 'Current Credit Balance', 'Monthly Debt', 'Credit Score']\n",
      "Число признаков - 16\n"
     ]
    }
   ],
   "source": [
    "target_name = 'Credit Default'\n",
    "cat_feature_names = [col for col in df_train.drop(target_name, axis=1).columns \\\n",
    "                     if len(df_train[col].unique()) < 16] \n",
    "print(f'Категориальные признаки: {cat_feature_names}')\n",
    "num_feature_names = [col for col in df_train.drop(target_name, axis=1).columns \\\n",
    "                     if col not in cat_feature_names]\n",
    "print(f'Количественные признаки: {num_feature_names}')\n",
    "selected_features = cat_feature_names + num_feature_names\n",
    "print(f'Число признаков - {len(selected_features)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc08994b",
   "metadata": {
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     "start_time": "2021-08-04T12:43:45.041539",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  Обработка пропусков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a7d900",
   "metadata": {
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    "tags": []
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   "source": [
    "Объекты пропусков в тренировочном и тестовом сетах одинаковы, а сами пропуски пропорциональны, предположим, что это обусловлено отсутствием возможности собрать данные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94a7e5d0",
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    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership                     0\n",
       "Annual Income                   1557\n",
       "Years in current job             371\n",
       "Tax Liens                          0\n",
       "Number of Open Accounts            0\n",
       "Years of Credit History            0\n",
       "Maximum Open Credit                0\n",
       "Number of Credit Problems          0\n",
       "Months since last delinquent    4081\n",
       "Bankruptcies                      14\n",
       "Purpose                            0\n",
       "Term                               0\n",
       "Current Loan Amount                0\n",
       "Current Credit Balance             0\n",
       "Monthly Debt                       0\n",
       "Credit Score                    1557\n",
       "Credit Default                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "091645bf",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership                     0\n",
       "Annual Income                    513\n",
       "Years in current job              86\n",
       "Tax Liens                          0\n",
       "Number of Open Accounts            0\n",
       "Years of Credit History            0\n",
       "Maximum Open Credit                0\n",
       "Number of Credit Problems          0\n",
       "Months since last delinquent    1358\n",
       "Bankruptcies                       3\n",
       "Purpose                            0\n",
       "Term                               0\n",
       "Current Loan Amount                0\n",
       "Current Credit Balance             0\n",
       "Monthly Debt                       0\n",
       "Credit Score                     513\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "978a2943",
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    "execution": {
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   "outputs": [],
   "source": [
    "df_train['Years in current job'].fillna(0, inplace=True)\n",
    "df_test['Years in current job'].fillna(0, inplace=True)\n",
    "df_train['Bankruptcies'].fillna(0, inplace=True)\n",
    "df_test['Bankruptcies'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf621f0",
   "metadata": {
    "papermill": {
     "duration": 0.017668,
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     "start_time": "2021-08-04T12:43:45.265966",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Из матрицы корреляций делаем вывод, что признак Annual Income коррелирует с признаком Monthly Debt. Заполним пропуски при помощи коэффициента, связывающего значения медиан применимо к вышеуказанным признакам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9b7ae6",
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   "source": [
    "Согласно предметной области значение Кредитного рейтинга не превышает 1000. Значения превышающие 1000 явно содержат ошибку. При просмотре данных значений видим, что у них справа добавлен лишний 0. Для исправления ошибки разделим данные значения на 10."
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership                object\n",
       "Years in current job          object\n",
       "Tax Liens                    float64\n",
       "Number of Credit Problems    float64\n",
       "Bankruptcies                 float64\n",
       "Purpose                       object\n",
       "Term                          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[cat_feature_names].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d9c19cf",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership               object\n",
       "Years in current job         object\n",
       "Tax Liens                     int64\n",
       "Number of Credit Problems     int64\n",
       "Bankruptcies                  int64\n",
       "Purpose                      object\n",
       "Term                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cat_feature_names:\n",
    "    if df_train[col].dtype == 'float64':\n",
    "        df_train[col] = df_train[col].astype('int')\n",
    "        df_test[col] = df_test[col].astype('int')\n",
    "\n",
    "df_test[cat_feature_names].dtypes"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5625, 16), (1875, 16), (5625,), (1875,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "X = df_train.drop(target_name, axis=1)\n",
    "y = df_train[target_name]\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, shuffle=True, \n",
    "                                                      test_size=0.25, \n",
    "                                                      random_state=42)\n",
    "X_train.shape, X_valid.shape, y_train.shape, y_valid.shape"
   ]
  },
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   "execution_count": 12,
   "id": "04481b2f",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.98      0.88      4074\n",
      "           1       0.86      0.32      0.47      1551\n",
      "\n",
      "    accuracy                           0.80      5625\n",
      "   macro avg       0.83      0.65      0.67      5625\n",
      "weighted avg       0.81      0.80      0.76      5625\n",
      "\n",
      "TEST\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.98      0.86      1313\n",
      "           1       0.84      0.31      0.45       562\n",
      "\n",
      "    accuracy                           0.77      1875\n",
      "   macro avg       0.80      0.64      0.65      1875\n",
      "weighted avg       0.79      0.77      0.74      1875\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def get_classification_report(y_train_true, y_train_pred, y_test_true, y_test_pred):\n",
    "    print('TRAIN\\n\\n' + classification_report(y_train_true, y_train_pred))\n",
    "    print('TEST\\n\\n' + classification_report(y_test_true, y_test_pred))\n",
    "    \n",
    "baseline = catb.CatBoostClassifier(iterations=100, \n",
    "                                   nan_mode='Min', \n",
    "                                   silent=True, \n",
    "                                   random_state=42)\n",
    "baseline.fit(X_train, y_train, \n",
    "             cat_features=cat_feature_names, \n",
    "             eval_set=(X_valid, y_valid)\n",
    "            )\n",
    "\n",
    "y_train_pred = baseline.predict(X_train)\n",
    "y_valid_pred = baseline.predict(X_valid)\n",
    "\n",
    "get_classification_report(y_train, y_train_pred, y_valid, y_valid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e7458b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-04T12:43:46.284457Z",
     "iopub.status.busy": "2021-08-04T12:43:46.276182Z",
     "iopub.status.idle": "2021-08-04T12:43:46.793075Z",
     "shell.execute_reply": "2021-08-04T12:43:46.793553Z",
     "shell.execute_reply.started": "2021-08-04T12:40:09.540839Z"
    },
    "papermill": {
     "duration": 0.545685,
     "end_time": "2021-08-04T12:43:46.793747",
     "exception": false,
     "start_time": "2021-08-04T12:43:46.248062",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_proba_calibration_plots(y_predicted_probs, y_true_labels):\n",
    "    preds_with_true_labels = np.array(list(zip(y_predicted_probs, y_true_labels)))\n",
    "\n",
    "    thresholds = []\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    f1_scores = []\n",
    "\n",
    "    for threshold in np.linspace(0.1, 0.9, 9):\n",
    "        thresholds.append(threshold)\n",
    "        precisions.append(precision_score(y_true_labels, list(map(int, y_predicted_probs > threshold))))\n",
    "        recalls.append(recall_score(y_true_labels, list(map(int, y_predicted_probs > threshold))))\n",
    "        f1_scores.append(f1_score(y_true_labels, list(map(int, y_predicted_probs > threshold))))\n",
    "         \n",
    "    figure = plt.figure(figsize = (10, 5))\n",
    "\n",
    "    plt1 = figure.add_subplot(111)\n",
    "    plt1.axhline(0.5, color=\"red\")\n",
    "    plt1.plot(thresholds, precisions, label='Precision', linewidth=4)\n",
    "    plt1.plot(thresholds, recalls, label='Recall', linewidth=4)\n",
    "    plt1.plot(thresholds, f1_scores, label='F1', linewidth=4)\n",
    "    plt1.set_ylabel('Scores')\n",
    "    plt1.set_xlabel('Probability threshold')\n",
    "    plt1.set_title('Probabilities threshold calibration')\n",
    "    plt1.legend(bbox_to_anchor=(0.25, 0.25));\n",
    "    \n",
    "y_valid_proba = baseline.predict_proba(X_valid)[:, 1]\n",
    "show_proba_calibration_plots(y_valid_proba, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb7ae6ba",
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    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.98      0.88      4074\n",
      "           1       0.86      0.32      0.47      1551\n",
      "\n",
      "    accuracy                           0.80      5625\n",
      "   macro avg       0.83      0.65      0.67      5625\n",
      "weighted avg       0.81      0.80      0.76      5625\n",
      "\n",
      "TEST\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80      1313\n",
      "           1       0.54      0.54      0.54       562\n",
      "\n",
      "    accuracy                           0.72      1875\n",
      "   macro avg       0.67      0.67      0.67      1875\n",
      "weighted avg       0.72      0.72      0.72      1875\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_valid_pred = np.array(y_valid_proba > 0.31, dtype=int)\n",
    "get_classification_report(y_train, y_train_pred, y_valid, y_valid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01ca4b88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-04T12:43:46.939835Z",
     "iopub.status.busy": "2021-08-04T12:43:46.938968Z",
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     "shell.execute_reply": "2021-08-04T12:43:47.540799Z",
     "shell.execute_reply.started": "2021-08-04T12:40:10.108690Z"
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     "exception": false,
     "start_time": "2021-08-04T12:43:46.888781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "baseline.fit(X, y, \n",
    "             cat_features=cat_feature_names)\n",
    "\n",
    "y_test_pred = baseline.predict_proba(df_test)[:, 1]\n",
    "y_test_pred = np.array(y_test_pred > 0.31, dtype=int)\n",
    "\n",
    "submit['Credit Default'] = y_test_pred\n",
    "submit.to_csv('baseline_catboost_predictions.csv', index=False)"
   ]
  }
 ],
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